Player performance analytics has become an essential tool in modern sports, helping teams make data-driven decisions for player selection, training, and strategy development. Traditional performance evaluation methods rely on subjective judgments, which often lack consistency and accuracy. With the rise of artificial intelligence (AI) and machine learning (ML), sports analytics has undergone a transformation, allowing real-time data processing, predictive analysis, and performance forecasting.This study proposes an AI-driven system for player performance analytics using computer vision, deep learning, and statistical modeling. The system captures key performance indicators (KPIs) such as speed, agility, endurance, accuracy, and reaction time, enabling detailed insights into a player’s strengths and weaknesses. The research is based on extensive datasets, AI models, and real- time monitoring tools to ensure accurate player evaluation. The proposed system is evaluated on a dataset containing professional player statistics, and results demonstrate that AI-based analytics significantly improve performance assessments.This study contributes to the field of sports science by integrating AI for objective and accurate performance evaluation. Additionally, it highlights the advantages of AI-driven insights in optimizing player training programs and enhancing competitive strategies.
Player Performance, Artificial Intelligence, Machine Learning, Sports Analytics, Deep Learning, Computer Vision, Data Science.
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